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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Experiences in Building a Composable and Functional API f...
Juan Fumero, György Rethy, Athanasios Stratikopoulos, Nikos Fout · 2023-05-16 · via cs.DC updates on arXiv.org

This paper presents the Beehive SPIR-V Toolkit; a framework that can automatically generate a Java composable and functional library for dynamically building SPIR-V binary modules. The Beehive SPIR-V Toolkit can be used by optimizing compilers and runtime systems to generate and validate SPIR-V binary modules from managed runtime systems, such as the Java Virtual Machine (JVM). Furthermore, our framework is architected to accommodate new SPIR-V releases in an easy-to-maintain manner, and it facilitates the automatic generation of Java libraries for other standards, besides SPIR-V. The Beehive SPIR-V Toolkit also includes an assembler that emits SPIR-V binary modules from disassembled SPIR-V text files, and a disassembler that converts the SPIR-V binary code into a text file, and a console client application. To the best of our knowledge, the Beehive SPIR-V Toolkit is the first Java programming framework that can dynamically generate SPIR-V binary modules. To demonstrate the use of our framework, we showcase the integration of the SPIR-V Beehive Toolkit in the context of the TornadoVM, a Java framework for automatically offloading and running Java programs on heterogeneous hardware. We show that, via the SPIR-V Beehive Toolkit, the TornadoVM is able to compile code 3x faster than its existing OpenCL C JIT compiler, and it performs up to 1.52x faster than the existing OpenCL C backend in TornadoVM.